An ML Optimisation Agent is a computer program or system that automatically improves the performance of machine learning models. It uses feedback and data to adjust the model’s parameters, settings, or strategies, aiming to make predictions more accurate or efficient. These agents can work by trying different approaches and learning from results, so they can…

Accuracy drops refer to a noticeable decrease in how well a system or model makes correct predictions or outputs. This can happen suddenly or gradually, and often signals that something has changed in the data, environment, or the way the system is being used. Identifying and understanding accuracy drops is important for maintaining reliable performance…

Model benchmarks are standard tests or sets of tasks used to measure and compare the performance of different machine learning models. These benchmarks provide a common ground for evaluating how well models handle specific challenges, such as recognising images, understanding language, or making predictions. By using the same tests, researchers and developers can objectively assess…

Model memory refers to the way an artificial intelligence model stores and uses information from previous interactions or data. It helps the model remember important details, context, or patterns so it can make better predictions or provide more relevant responses. Model memory can be short-term, like recalling the last few conversation turns, or long-term, like…